Discrimination-aware data mining: a survey

Asmita Kashid, Vrushali Kulkarni and Ruhi Patankar

International Journal of Data Science, 2017, vol. 2, issue 1, 70-84

Abstract: Data mining is a very important and useful technique to extract knowledge from raw data. However, there is a challenge faced by data mining researchers, in the form of potential discrimination. Discrimination means giving unfair treatment to a person just because one belongs to a minority group, without considering one's individual merit or qualification. The results extracted using data mining techniques may lead to discrimination, if a biased historical/training dataset is used. It is very important to prevent data mining technique from becoming a source of discrimination. A detailed survey of discrimination discovery methods and discrimination prevention methods is presented in this paper. This paper also presents the list of datasets used for experiments in different discrimination-aware data mining (DADM) approaches. Some ideas for future research work that may help in preventing discrimination are also discussed.

Keywords: DADM; discrimination-aware data mining; discrimination discovery; discrimination prevention; biased datasets; bias. (search for similar items in EconPapers)
Date: 2017
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